Mouse RNA plots
Erica Valentini
8/16/2021
Load
library(readr)
library(tidyverse)
library(ggforce)
library(fgsea)
library(msigdbr)
library(ggrepel)
library(cowplot)
library(viridis)
library(ggpubr)
library(ggExtra)
library(RColorBrewer)
library(waffle)
library(pheatmap)
library(dendextend)
de_single_cell_dt_raw <- read_delim("../../scRNA-Seq-invivo/processed_data/DE_samples_as_replicates.tsv.gz",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
fgsea_hallmarktdy_exvivo <- read_delim("../../RNA-Seq-exvivo/Results/fgsea_hallmarktdy_exvivo.txt",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
fgsea_hallmarktdy_invivo <- read_delim("../../RNA-Seq-invivo/results/fgsea_hallmarktdy_invivo.txt",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
invivo_count_table <- read_delim("../../RNA-Seq-invivo/raw_data/merged_gene_counts.txt",
escape_double = FALSE, trim_ws = TRUE)
invivo_metadata <- read_delim("../../RNA-Seq-invivo/raw_data/19397_meta.tsv", delim = "\t",
escape_double = FALSE, trim_ws = TRUE)
bulk_invivo_all_genes <- read_delim("../../RNA-Seq-invivo/results/bulk_invivo_all genes.txt",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
sc_meta_and_reductions_tsv <- read_delim("../../scRNA-Seq-invivo/processed_data/meta_and_reductions.tsv.gz",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
bulk_organoids_all_genes <- read_delim("../../RNA-Seq-exvivo/Results/bulk_organoids_all_genes.txt",
delim = "\t", escape_double = FALSE, trim_ws = TRUE)
b110_grey = "#808080"
b110_grey_light = "#909090"
b110_transparent_black = alpha("#000000", 0.5)
google_red = "#dd4b39"
google_green = "#0F9D58"
google_yellow = "#F4B400"
google_blue = "#4285F4"
maja_red = alpha("#E64B35CC", 0.8)
maja_blue = alpha("#4DBBD5CC", 0.8)
maja_young = "#91D1C2CC"
maja_youngD = "#00A087CC"
maja_aged = "#8491B4CC"
maja_agedD = "#3C5488CC"
levels_of_cell_type <- c("pseudo bulk", "dead cells", "Stem", "TA", "Enterocyte Progenitor",
"Enterocyte", "EEC", "Paneth", "Goblet & Paneth", "Goblet", "Tuft")
labels_of_cell_type <- c("pseudo bulk", "dead cells", "Stem cells", "TA cells", "EC prog.",
"Enterocytes", "EECs", "Paneth cells", "Goblet &\nPaneth cells", "Goblet cells",
"Tuft cells")
colors_of_cell_type <- c("#303030", "#303030", "#66a61e", "#1b9e77", "#418DA6", "#7570b3",
"#a846a0", "#ce5a02", "#a6761d", "#e6ab02", "#FE5D26") # old tuft: e7298a #E53D00
names(colors_of_cell_type) <- labels_of_cell_typeModify de_single_cell input file:
de_single_cell_dt <- de_single_cell_dt_raw %>%
filter(contrast == "condition_young_vs_aged") %>%
dplyr::rename(log2FoldChange = "estimate", lfcSE = "stderror", stat = "statistic",
pvalue = "p.value", padj = "p.adjusted") %>%
filter(cell_type != "pseudo bulk")DotPlot cell types enriched
cell_type_order <- c("Stem cells", "TA cells", "EC prog.", "Enterocytes", "EECs",
"Paneth cells", "Goblet cells", "Tuft cells")
de_single_cell_dt <- de_single_cell_dt %>%
mutate(cell_type_final = ifelse(cell_type %in% c("EEC", "Enterocyte Progenitor",
"Enterocyte"), cell_type, paste(cell_type, "cells", sep = " "))) %>%
mutate(cell_type_final = case_when(cell_type == "Enterocyte" ~ "Enterocytes",
cell_type == "EEC" ~ "EECs", cell_type == "Enterocyte Progenitor" ~ "EC prog.",
TRUE ~ cell_type_final)) %>%
mutate(cell_type = cell_type_final)
m_df <- msigdbr(species = "Mus musculus", category = "H")
# format them for fgsea
m_list = m_df %>%
split(x = .$gene_symbol, f = .$gs_name)
list_filter <- c("HALLMARK_INTERFERON_GAMMA_RESPONSE", "HALLMARK_INTERFERON_ALPHA_RESPONSE",
"HALLMARK_ALLOGRAFT_REJECTION", "HALLMARK_TNFA_SIGNALING_VIA_NFKB", "HALLMARK_INFLAMMATORY_RESPONSE",
"HALLMARK_IL6_JAK_STAT3_SIGNALING", "HALLMARK_UV_RESPONSE_UP", "HALLMARK_P53_PATHWAY",
"HALLMARK_APOPTOSIS", "HALLMARK_MTORC1_SIGNALING", "HALLMARK_UNFOLDED_PROTEIN_RESPONSE",
"HALLMARK_FATTY_ACID_METABOLISM", "HALLMARK_XENOBIOTIC_METABOLISM", "HALLMARK_MYC_TARGETS_V1",
"HALLMARK_MITOTIC_SPINDLE", "HALLMARK_G2M_CHECKPOINT", "HALLMARK_WNT_BETA_CATENIN_SIGNALING",
"HALLMARK_HYPOXIA")
cell_type_dotplot <- c("Stem cells", "TA cells", "EC prog.", "Enterocytes", "Goblet cells")
cell_types <- de_single_cell_dt %>%
distinct(cell_type) %>%
pull(cell_type)
cell_type_fgsea_df <- vector(mode = "list", length = length(cell_types))
for (type in seq(1, length(cell_types))) {
seq_results <- de_single_cell_dt %>%
filter(cell_type == cell_types[[type]]) %>%
select(gene, stat) %>%
mutate(stat = -stat) %>%
deframe()
fgsea_hallmark <- fgsea(pathways = m_list, stats = seq_results)
fgsea_hallmarktdy <- fgsea_hallmark %>%
as_tibble() %>%
arrange(desc(NES))
cell_type_fgsea_df[[type]] <- fgsea_hallmarktdy
}
names(cell_type_fgsea_df) <- cell_types
cell_type_all_hallmarks <- cell_type_fgsea_df %>%
bind_rows(.id = "cell_type") %>%
mutate(cell_type = factor(cell_type, levels = cell_type_order)) %>%
filter(padj < 0.05) %>%
ggplot(aes(x = cell_type, y = pathway, color = NES, size = -log10(padj))) + geom_point() +
scale_colour_gradient2(high = "red", low = "blue") + theme_cowplot() + theme(panel.grid.major.y = element_line(size = 0.25,
linetype = "solid", colour = "gray90")) + ylab("Hallmark Pathway") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
cell_type_all_hallmarks
ggsave(cell_type_all_hallmarks, filename = "../Graphics/sc_cell_type_all_hallmarks.pdf",
device = "pdf", bg = "white", width = 2600, height = 2600, units = "px")
cell_type_selected_hallmarks <- cell_type_fgsea_df %>%
bind_rows(.id = "cell_type") %>%
filter(padj < 0.05) %>%
filter(pathway %in% list_filter) %>%
mutate(pathway = factor(pathway, levels = rev(list_filter))) %>%
filter(cell_type %in% cell_type_dotplot) %>%
mutate(cell_type = factor(cell_type, levels = cell_type_dotplot)) %>%
ggplot(aes(x = cell_type, y = pathway, color = NES, size = -log10(padj))) + geom_point() +
scale_colour_gradient2(high = "red", low = "blue") + theme_cowplot() + theme(panel.grid.major.y = element_line(size = 0.25,
linetype = "solid", colour = "gray90")) + ylab("Hallmark Pathway") + theme(axis.text.x = element_text(angle = 45,
hjust = 1))
cell_type_selected_hallmarks
ggsave(cell_type_selected_hallmarks, filename = "../Graphics/sc_cell_type_selected_hallmarks.pdf",
device = "pdf", bg = "white", width = 2400, height = 2000, units = "px")Enrichplot
dotplot <- fgsea_hallmarktdy_invivo %>%
filter(padj < 0.05) %>%
filter(pathway %in% list_filter) %>%
ggplot(aes(y = reorder(pathway, NES), x = NES, color = padj, size = size)) +
geom_point() + ylab("Pathway") + scale_color_viridis() + theme_cowplot() + geom_vline(xintercept = 0,
size = 0.25, linetype = "solid", colour = "gray70") + theme(panel.grid.major.y = element_line(size = 0.25,
linetype = "solid", colour = "gray90"))
dotplot
ggsave(dotplot, filename = "../Graphics/gsea_dotplot.pdf", device = "pdf", bg = "white",
width = 2400, height = 2000, units = "px")
dotplot_inverted <- fgsea_hallmarktdy_invivo %>%
filter(padj < 0.05) %>%
filter(pathway %in% list_filter) %>%
ggplot(aes(y = reorder(pathway, NES), x = NES, color = padj, size = size)) +
geom_point() + ylab("Pathway") + scale_color_viridis(direction = -1) + theme_cowplot() +
geom_vline(xintercept = 0, size = 0.25, linetype = "solid", colour = "gray70") +
theme(panel.grid.major.y = element_line(size = 0.25, linetype = "solid", colour = "gray90"))
dotplot_inverted
ggsave(dotplot_inverted, filename = "../Graphics/gsea_dotplot_inverted.pdf", device = "pdf",
bg = "white", width = 2400, height = 2000, units = "px")
sidebyside <- fgsea_hallmarktdy_invivo %>%
mutate(type = "invivo") %>%
filter(padj < 0.05) %>%
rbind(fgsea_hallmarktdy_exvivo %>%
mutate(type = "exvivo") %>%
filter(padj < 0.05)) %>%
mutate(type = fct_relevel(type, "invivo")) %>%
filter(pathway %in% list_filter) %>%
mutate(pathway = factor(pathway, levels = rev(list_filter))) %>%
ggplot(aes(x = type, y = pathway, color = NES, size = -log10(padj))) + geom_point() +
scale_colour_gradient2(high = "red", low = "blue") + theme_cowplot() + theme(panel.grid.major.y = element_line(size = 0.25,
linetype = "solid", colour = "gray90")) + ylab("Hallmark Pathway")
sidebyside
ggsave(sidebyside, filename = "../Graphics/gsea_dotplot_sidebyside.pdf", device = "pdf",
bg = "white", width = 2400, height = 1800, units = "px")Heatmaps
invivo_count_table <- invivo_count_table[rowSums(invivo_count_table %>%
select(!c("Geneid", "gene_name"))) > 1, ]
genes_set_1 <- c("H2-Aa", "H2-Ab1", "H2-DMa", "H2-DMb1", "H2-Eb1", "Ceacam10", "Cd74",
"Ciita")
data_subset <- invivo_count_table %>%
as_tibble() %>%
filter(gene_name %in% genes_set_1) %>%
arrange(gene_name) %>%
column_to_rownames(var = "gene_name") %>%
select(-Geneid) %>%
as.matrix()
annotation <- invivo_metadata %>%
select(FASTQ_FILE, SAMPLE_NAME) %>%
filter(FASTQ_FILE != "Undetermined_1.fastq.gz") %>%
separate(SAMPLE_NAME, into = c("sample_name", "type"), sep = "-", remove = FALSE) %>%
mutate(FASTQ_FILE = substring(FASTQ_FILE, 1, nchar(FASTQ_FILE) - 12)) %>%
mutate(count_column_names = paste0(FASTQ_FILE, "Aligned.sortedByCoord.out.bam")) %>%
arrange(match(count_column_names, colnames(data_subset)))
identical(annotation$count_column_names, colnames(data_subset))## [1] TRUEannotation <- annotation %>%
column_to_rownames(var = "SAMPLE_NAME")
colnames(data_subset) <- rownames(annotation)
cal_z_score <- function(x) {
(x - mean(x))/sd(x)
}
anno_colors = list(type = c(Y = maja_young, A = maja_aged))
data_subset_norm <- t(apply(data_subset, 1, cal_z_score))
callback = function(hc, mat) {
sv = svd(t(mat))$v[, 1]
dend = reorder(as.dendrogram(hc), wts = sv)
as.hclust(dend)
}
data_subset_norm <- t(apply(data_subset, 1, cal_z_score))
heatmap1 <- pheatmap(data_subset_norm)
col_dend <- heatmap1$tree_col
col_dend <- dendextend::rotate(col_dend, order = rev(colnames(data_subset_norm)[heatmap1$tree_col$order]))
pheatmap(data_subset_norm, annotation_col = annotation %>%
select(type), color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
annotation_colors = anno_colors, cluster_cols = as.hclust(col_dend), cluster_rows = FALSE,
clustering_callback = callback, cellwidth = 60, cellheight = 60, filename = "../Graphics/heatmap1.pdf")
genes_set_2 <- c("Gbp2", "Gbp6", "Gbp7", "Gbp8", "Gbp9")
data_subset <- invivo_count_table %>%
as_tibble() %>%
filter(gene_name %in% genes_set_2) %>%
arrange(gene_name) %>%
column_to_rownames(var = "gene_name") %>%
select(-Geneid) %>%
as.matrix()
identical(annotation$count_column_names, colnames(data_subset))## [1] TRUEcolnames(data_subset) <- rownames(annotation)
data_subset_norm <- t(apply(data_subset, 1, cal_z_score))
pheatmap(data_subset_norm, annotation_col = annotation %>%
select(type), color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdBu")))(100),
annotation_colors = anno_colors, cluster_rows = FALSE, cellwidth = 80, cellheight = 80,
filename = "../Graphics/heatmap2.pdf")Cell type percentage: waffle chart
In the following plot, each square represents a 0.5% of the cells
round_preserve_sum <- function(x, digits = 0) {
x <- x
y <- floor(x)
indices <- tail(order(x - y), round(sum(x)) - sum(y))
y[indices] <- y[indices] + 1
y
}
waffle_data <- sc_meta_and_reductions_tsv %>%
group_by(cell_type) %>%
dplyr::count() %>%
ungroup() %>%
mutate(perc_2 = round_preserve_sum(n/sum(n) * 200)) %>%
mutate(percentage = round(n/sum(n) * 100, 1)) %>%
left_join(tibble(cell_type = levels_of_cell_type, cell_type_name = labels_of_cell_type)) %>%
mutate(cell_type_name = factor(cell_type_name, levels = rev(cell_type_order))) %>%
arrange(cell_type_name)
write_tsv(waffle_data, file = "../Results/cell_type_stats_waffle.tsv")
waffle_plot <- waffle_data %>%
ggplot(aes(values = perc_2, fill = cell_type_name)) + geom_waffle(colour = "white",
flip = TRUE) + coord_equal() + scale_fill_manual(values = colors_of_cell_type[!names(colors_of_cell_type) %in%
c("pseudo bulk", "dead cells", "Goblet &\nPaneth cells")]) + theme_void()
waffle_plot
ggsave(waffle_plot, filename = "../Graphics/waffle_plot.pdf", device = "pdf", bg = "white")DotPlot cell types MHC
genes_set_1 <- c("H2-Aa", "H2-Ab1", "H2-DMa", "H2-DMb1", "H2-Eb1", "Ceacam10", "Cd74",
"Ciita")
dotplot_mhc <- de_single_cell_dt %>%
mutate(cell_type = factor(cell_type, levels = rev(cell_type_order))) %>%
filter(gene %in% genes_set_1) %>%
ggplot(aes(y = cell_type, x = gene, color = -(log2FoldChange), size = -log10(padj))) +
geom_point() + scale_colour_viridis(option = "magma", direction = -1) + theme_cowplot() +
ylab("MHC genes") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
dotplot_mhc
ggsave(dotplot_mhc, filename = "../Graphics/dotplot_mhc.pdf", device = "pdf", bg = "white")Correlation plot
correlation_plot <- bulk_invivo_all_genes %>%
full_join(bulk_organoids_all_genes, by = c("gene_name"), suffix = c(".iv", ".ev")) %>%
filter(padj.iv < 0.1, padj.ev < 0.1) %>%
ggscatter(x = "log2FoldChange.iv", y = "log2FoldChange.ev", add = "reg.line",
conf.int = TRUE, add.params = list(color = "blue", fill = "lightgray"), color = "gray10",
alpha = 0.6) + geom_rug(side = "bi", color = "gray10") + stat_cor(method = "pearson") +
coord_fixed()
correlation_plot
ggsave(correlation_plot, filename = "../Graphics/correlation_plot.pdf", device = "pdf",
bg = "white")Volcano Plots
shared_21_genes <- c("Cd74", "H2-Ab1", "1700011H14Rik", "H2-Eb1", "Muc2", "Irf1",
"H2-Aa", "Ly6e", "Igtp", "Tifa", "H2-DMb1", "Psmb8", "Nrn1", "Prss32", "Ciita",
"Sp140", "H2-DMa", "Oat", "H2-Q7", "Parp3", "Sepp1")
volcano_sc_pseudobulk <- de_single_cell_dt_raw %>%
filter(contrast == "condition_young_vs_aged") %>%
filter(cell_type == "pseudo bulk") %>%
mutate(signif = if_else(p.adjusted <= 0.1, if_else(-estimate > 0, "up", "down"),
"none")) %>%
mutate(signif = if_else(abs(estimate) < 0.5, "none", signif)) %>%
tidyr::drop_na() %>%
ggplot(aes(x = -estimate, y = -log10(p.value), col = signif, name = gene)) +
geom_point() + xlab("log2FoldChange") + ylab("-log10(pvalue)") + ggtitle("Aged_Over_Young_Top15_Pseudobulk") +
scale_color_manual(values = c(maja_blue, b110_grey, maja_red)) + geom_label_repel(data = de_single_cell_dt_raw %>%
filter(contrast == "condition_young_vs_aged") %>%
filter(cell_type == "pseudo bulk") %>%
arrange(desc(abs(statistic))) %>%
head(15), aes(label = gene), min.segment.length = 0, col = "black") + geom_vline(xintercept = c(0.5,
-0.5), lty = 2) + geom_hline(yintercept = 2, lty = 2) + theme_cowplot()
volcano_sc_pseudobulk
ggsave(volcano_sc_pseudobulk, filename = "../Graphics/volcano_sc_pseudobulk.pdf",
device = "pdf", bg = "white")
volcano_sc_pseudobulk_21genes <- de_single_cell_dt_raw %>%
filter(contrast == "condition_young_vs_aged") %>%
filter(cell_type == "pseudo bulk") %>%
mutate(signif = if_else(p.adjusted <= 0.1, if_else(-estimate > 0, "up", "down"),
"none")) %>%
mutate(signif = if_else(abs(estimate) < 0.5, "none", signif)) %>%
tidyr::drop_na() %>%
ggplot(aes(x = -estimate, y = -log10(p.value), col = signif, name = gene)) +
geom_point() + xlab("log2FoldChange") + ylab("-log10(pvalue)") + ggtitle("Aged_Over_Young_21_genes_in_common_Pseudobulk") +
scale_color_manual(values = c(maja_blue, b110_grey, maja_red)) + geom_label_repel(data = de_single_cell_dt_raw %>%
filter(gene %in% shared_21_genes) %>%
filter(cell_type == "pseudo bulk"), aes(label = gene), min.segment.length = 0,
col = "black") + geom_vline(xintercept = c(0.5, -0.5), lty = 2) + geom_hline(yintercept = 2,
lty = 2) + theme_cowplot()
volcano_sc_pseudobulk_21genes
ggsave(volcano_sc_pseudobulk_21genes, filename = "../Graphics/volcano_sc_pseudobulk_21genes.pdf",
device = "pdf", bg = "white")
for (type in unique(de_single_cell_dt$cell_type)) {
print(type)
volcano <- de_single_cell_dt %>%
filter(cell_type == type) %>%
mutate(signif = if_else(padj <= 0.1, if_else(-log2FoldChange > 0, "up", "down"),
"none")) %>%
mutate(signif = if_else(abs(log2FoldChange) < 0.5, "none", signif)) %>%
tidyr::drop_na() %>%
ggplot(aes(x = -log2FoldChange, y = -log10(pvalue), col = signif, name = gene)) +
geom_point() + xlab("log2FoldChange") + ggtitle(paste0("Aged_Over_Young_Top20_",
type)) + scale_color_manual(values = c(maja_blue, b110_grey, maja_red)) +
geom_label_repel(data = de_single_cell_dt %>%
filter(cell_type == type) %>%
arrange(desc(abs(stat))) %>%
head(20), aes(label = gene), min.segment.length = 0, col = "black") +
geom_vline(xintercept = c(0.5, -0.5), lty = 2) + geom_hline(yintercept = 2,
lty = 2) + theme_cowplot()
print(volcano)
ggsave(volcano, filename = paste0("../Graphics/volcano_sc_", type, ".pdf"), device = "pdf",
bg = "white")
}## [1] "TA cells"
## [1] "Stem cells"
## [1] "EC prog."
## [1] "Goblet cells"
## [1] "Enterocytes"
## [1] "Paneth cells"
## [1] "EECs"
## [1] "Tuft cells"
SessionInfo
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Gentoo/Linux
##
## Matrix products: default
## BLAS: /usr/lib64/libblas.so.3.9.0
## LAPACK: /usr/lib64/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=C.UTF8 LC_NUMERIC=C LC_TIME=C.UTF8
## [4] LC_COLLATE=C.UTF8 LC_MONETARY=C.UTF8 LC_MESSAGES=C.UTF8
## [7] LC_PAPER=C.UTF8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dendextend_1.15.2 pheatmap_1.0.12 waffle_1.0.1 RColorBrewer_1.1-2
## [5] ggExtra_0.9 ggpubr_0.4.0 viridis_0.6.2 viridisLite_0.4.0
## [9] cowplot_1.1.1 ggrepel_0.9.1 msigdbr_7.4.1 fgsea_1.18.0
## [13] ggforce_0.3.3 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
## [17] purrr_0.3.4 tidyr_1.1.4 tibble_3.1.5 ggplot2_3.3.5
## [21] tidyverse_1.3.1 readr_2.0.2
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-2 ggsignif_0.6.3 ellipsis_0.3.2
## [4] rio_0.5.27 fs_1.5.0 rstudioapi_0.13
## [7] farver_2.1.0 bit64_4.0.5 DT_0.19
## [10] fansi_0.5.0 lubridate_1.8.0 xml2_1.3.2
## [13] splines_4.1.0 extrafont_0.17 knitr_1.36
## [16] polyclip_1.10-0 jsonlite_1.7.2 broom_0.7.10
## [19] Rttf2pt1_1.3.9 dbplyr_2.1.1 shiny_1.7.1
## [22] compiler_4.1.0 httr_1.4.2 backports_1.3.0
## [25] assertthat_0.2.1 Matrix_1.3-3 fastmap_1.1.0
## [28] cli_3.1.0 later_1.3.0 tweenr_1.0.2
## [31] formatR_1.11 htmltools_0.5.2 tools_4.1.0
## [34] gtable_0.3.0 glue_1.4.2 fastmatch_1.1-3
## [37] Rcpp_1.0.7 carData_3.0-4 cellranger_1.1.0
## [40] jquerylib_0.1.4 vctrs_0.3.8 nlme_3.1-152
## [43] babelgene_21.4 extrafontdb_1.0 xfun_0.27
## [46] openxlsx_4.2.4 rvest_1.0.2 mime_0.12
## [49] miniUI_0.1.1.1 lifecycle_1.0.1 rstatix_0.7.0
## [52] MASS_7.3-54 scales_1.1.1 vroom_1.5.5
## [55] ragg_1.2.0 hms_1.1.1 promises_1.2.0.1
## [58] parallel_4.1.0 yaml_2.2.1 curl_4.3.2
## [61] gridExtra_2.3 sass_0.4.0 stringi_1.7.5
## [64] highr_0.9 zip_2.2.0 BiocParallel_1.26.2
## [67] systemfonts_1.0.3 rlang_0.4.12 pkgconfig_2.0.3
## [70] evaluate_0.14 lattice_0.20-44 labeling_0.4.2
## [73] htmlwidgets_1.5.4 bit_4.0.4 tidyselect_1.1.1
## [76] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
## [79] generics_0.1.1 DBI_1.1.1 mgcv_1.8-35
## [82] pillar_1.6.4 haven_2.4.3 foreign_0.8-81
## [85] withr_2.4.2 abind_1.4-5 modelr_0.1.8
## [88] crayon_1.4.2 car_3.0-11 utf8_1.2.2
## [91] tzdb_0.2.0 rmarkdown_2.11 grid_4.1.0
## [94] readxl_1.3.1 data.table_1.14.2 reprex_2.0.1
## [97] digest_0.6.28 xtable_1.8-4 httpuv_1.6.3
## [100] textshaping_0.3.6 munsell_0.5.0 bslib_0.3.1